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2023 Vol. 42, No. 5 Published: 2023-10-20

Reviews Communications Regular Papers

Regular Papers

	513	Analysis of Resting-State EEG Specific Characteristics in Schizophrenic with RefractoryAuditory Hallucination
		Liu Shuang, Liao Jingmeng, Wang Xiaojuan, Li Meijuan, Gao Ying, Li Jie, Ming Dong
		DOI: 10.3969/j.issn.0258-8021.2023.05.001
		Schizophrenia is a heavy mental disease that seriously harms human physical and mental health. Auditory hallucination is one of core symptoms for schizophrenia, about 30% of patients with auditory hallucinations cannot be cured by drug treatment, called refractory auditory hallucinations (RAH). In order to explore the formation mechanism of refractory auditory hallucinations and the characteristics of their brain activity, 30 patients with refractory auditory hallucinations and 30 patients with non-refractory auditory hallucinations (NRAH) were enrolled. In this study, the 8-minute resting paradigm with eyes open and closed was adopted, and 64 channels of EEG (electroencephalography) data was collected at the same time. Then the absolute power spectrum of six frequency bands and the distribution of cortical EEG of the two groups were calculated by power spectrum and source localization analysis. Experimental results showed that the δ band was more activated in the right superior temporal gyrus, middle temporal gyrus, superior temporal sulcus and angular gyrus in RAH than in NRAH, but the average power of whole brain was significantly lower than that of NRAH group (p(RAH)=64.05±116.82, p(NRAH)=110.40±125.56, P<0.01). The absolute power of low-γ (p(RAH)=7.14±14.88, p(NRAH)= 8.99±10.13, P<0.05) and high-γ (p(RAH)=11.46±17.48, p(NRAH)=30.12±46.88, P<0.01) was significantly lower than that of NRAH, and the intensity and range of activation of them in the middle temporal gyrus, superior temporal to temporoparietal lobe, and frontoparietal junction were lower than those in NRAH, and the phenomenon of lateralization was absent in NRAH. In conclusion, δ, low-γ and high-γ may be potential physiological indicators of refractory auditory hallucinations, and the formation mechanism of refractory auditory hallucinations may be related to the overactivation of slow waves (especially δ) in the temporal lobe and the inactivation of high-frequency oscillations (especially γ) in the temporal lobe. The results of this study can provide effective objective basis for exploring the brain activity characteristics of patients with refractory auditory hallucinations and the disease mechanism of refractory auditory hallucinations, which has certain theoretical significance and clinical value.
		2023 Vol. 42 (5): 513-519 [Abstract] ( 149 ) HTML (1 KB) PDF (4135 KB) ( 130 )

	520	Effect of Direct Current Gavalnic Vestibular Stimulation on Resting State Functional Network of EEG
		Geng Yuehua, Li Chaoran, Xu Guizhi
		DOI: 10.3969/j.issn.0258-8021.2023.05.002
		This work is aimed to establish a resting functional network based on EEG signals to investigate effects of DC galvanic vestibular stimulation (GVS) with different intensities on the connectivity of resting functional network to reveal the mechanism of vestibular DC stimulation regulating vestibular dysfunction from the perspective of EEG connectivity and topology. 30 subjects were selected, and the experimental group was given 0.5, 1, 2 mA by vestibular direct current, and sham stimulation was applied to the control group to collect resting EEG signals before and after the stimulation with different current intensities. The Pearson correlation coefficient method was used to construct the correlation matrix. A brain network topology connection diagram with a threshold value of 0.44 was constructed to investigate the brain network topology attributes within the threshold value range of 0.44~0.84, and the impact of GVS of 0.5, 1, 2 mA on the brain network was explored. Compared with applying false stimulation, the effect of 1 mA vestibular electrical stimulation exerted the most significant effect. The clustering coefficient, node degree, global efficiency, and characteristic path length of the brain network after sham stimulation were 0.309 ± 0.023, 10.760 ± 1.502, 0.296 ± 0.014, 0.547 ± 0.018, and the clustering coefficient, node degree, global efficiency, and characteristic path length of the brain network after 1 mA vestibular electrical stimulation were 0.296 ± 0.014, 0.299 ± 0.014, 0.301 ± 0.014, and 0.299 ± 0.012, respectively (P<0.05). In conclusion, vestibular direct current stimulation was able to change the brain network connection and the brain network topology attribute of the brain. The GVS of 0.5, 1, 2 mA improved the efficiency of information transmission between the brain regions and the speed of information transmission and enhanced the strength of functional connection. Among them, the GVS stimulation of 1 mA exerted the most significant effect. This study provided a new idea for effective intervention of brain functional network activities in the treatment of vestibular diseases.
		2023 Vol. 42 (5): 520-528 [Abstract] ( 156 ) HTML (1 KB) PDF (10297 KB) ( 32 )

	529	Joint Bi-Projection Domain Adaptation and Graph-Based Semi-Supervised Label Estimation for EEG Emotion Recognition
		Li Wenzheng, Wang Wenjuan, Peng Yong, Kong Wanzeng
		DOI: 10.3969/j.issn.0258-8021.2023.05.003
		Electroencephalogram (EEG) has been widely used for objective emotion recognition because it is generated from the neural activities of central nervous system and is hard to camouflage. An obvious limitation is that the weak and non-stationary properties of EEG can cause the individual differences in emotion recognition. To this end, transfer learning models have been introduced to deal with this dilemma. However, the existing models are not able to couple the feature adaptation process with the target label estimation process, and on the other hand, they focused only on the recognition accuracy and have no sufficient investigation to the learned shared subspace. To solve these problems, this paper proposed a joint bi-projection domain adaptation and graph-based semi-supervised label estimation model for EEG emotion recognition (termed RAGE). We evaluated the effectiveness of the proposed RAGE model on the benchmark SEED-IV emotional data set, the data set was collected by playing films with obvious emotional tendencies for 15 subjects at three different Sessions. Results showed that the average recognition accuracies of the three sessions (77.7%、78.5%、79.6%) were much better than many of the existing transfer learning models. Specifically, compared with the classical joint domain adaptation (JDA) method, the average recognition accuracy has been greatly improved (Session2: 53.7% vs. 78.5%). In comparison with the four recently proposed models, RAGE obtained a minimum accuracy improvement of 8.90% (Session2 vs MEKT, manifold embedded knowledge transfer). By investigating the learned common subspace from the feature importance perspective, we achieved more insights to the occurrence of affective effects. That is, the average result showed that the importance of γ was greater than the other four bands, and the significant differences with the other four frequency bands were verified by one-way ANOVA (P<0.05); the brain topographic map showed that the (central) parietal lobe brain region had a higher weight than the other brain regions. Simultaneously, a study was conducted on single class emotional EEG activation patterns using label specific feature learning algorithms. In summary, this research provided a reference for the study and analysis of EEG emotion activation mode.
		2023 Vol. 42 (5): 529-541 [Abstract] ( 126 ) HTML (1 KB) PDF (11583 KB) ( 117 )

	542	Recognition of Major Depression Using Machine Learning Methods Based on Behavioral and Event-Related Potentials
		Hou Feng, Zhang Ming, Lin Xiangbin, Zhang Wei, Ma Rong
		DOI: 10.3969/j.issn.0258-8021.2023.05.004
		Major depressive disorder (MDD) is a mental disorder caused by a variety of factors, such as congenital genetic abnormalities, acquired environmental mutations. MDD can cause serious damage to patients’ daily life and social economy. Therefore, seeking more effective and objective physiological indicators and diagnostic methods to assist diagnosis has great significance for the early diagnosis and prevention of MDD. Based on the emotional face-word stroop task, this paper used traditional machine learning, ensemble learning and deep learning methods to study 31 MDD patients and 31 healthy controls by collecting behavioral data and ERP data of subjects. The evaluation indicators for different classification methods include accuracy (ACC), F1-score (F1), recall (Recall), specificity (Specificity), positive predictive value (PPV), and negative predictive value (NPV). The data set was randomly divided into training set, verification set and test set in a ratio of 7:2:1. The process was repeated ten times, and the final classification result was the mean ± standard deviation of ten times. The results showed that the accuracy of convolutional neural network (CNN) method, which can automatically learn and extract features from the data, achieved 89.76% ± 19.18% in the identification of MDD based on behavioral data. Based on ERP data, it was found that CNN obtained the optimal result under all six indicators, and the accuracy of MDD recognition was 90.71% ± 14.17%. This paper proposed a multi-modal deep learning neural network based on behavioral data and ERP data, which is referred as behavior-ERP parallel temporal convolution neural network (BEPTCNN). It achieved excellent results in all indicators of MDD identification task, and the recognition accuracy could reach 95.48% ± 7.31%. These results showed that both behavioral data and ERP data could be used as effective physiological indicators for the auxiliary diagnosis of MDD. In addition, the BEPTCNN model proposed in this paper could be used as an effective method for the recognition of MDD.
		2023 Vol. 42 (5): 542-553 [Abstract] ( 151 ) HTML (1 KB) PDF (5723 KB) ( 236 )

	554	Investigation on Actual Driver Fatigue Based on Combination of Multi-Characteristics
		Wang Lin, Wang Hong, Fu Rongrong, Yin Xiaowei, Liu Jintao
		DOI: 10.3969/j.issn.0258-8021.2023.05.005
		In order to discriminate driver fatigue accurately in real-time and reduce the traffic accidents caused by driver fatigue, physiological signals of 12 subjects in actual driving were recorded by wireless body area network (WBAN), and approximate entropy (ApEn) of electroencephalograph (EEG), electromyography (EMG) and respiration (RESP) signals were extracted. The upper trapeziuses at 2 cm of both sides of 6^th spinous process were determined as the data acquisition positions of EMG based on distortion energy density (DED) theory. Then the discriminant degree of their combination was analyzed by the fuzzy C-clustering method. Finally, a discriminant model on driver fatigue was built based on Mahalanobis distance theory. The experimental results showed that the decreasing trend of the upper trapezius at 6^th spinous process was more obvious than that at 7^th spinous process, and the significant index P<0.05, indicating the muscles at 6^th spinous process were more sensitive for driver fatigue. The actual testing result was consistent with the calculation result of DED theory, and verified the correctness of acquisition position of EMG. During the actual driving, the ApEns of EEG, EMG and RESP signals decreased. After about 90 min, the decreasing trend slowed down, indicating the deeper fatigue. By the fuzzy C-clustering analysis, in the case of the combination of EEG-EMG, obvious discrimination of the probability distribution between normal and fatigued state were detected, and they were selected as independent variables. Finally, a discriminant model on driver fatigue based on Mahalanobis distance theory was built, and its accuracy was up to 90.92%, which effectively discriminated the driver fatigue.
		2023 Vol. 42 (5): 554-562 [Abstract] ( 148 ) HTML (1 KB) PDF (3469 KB) ( 386 )

	563	Changes of EEG Microstate in Patients with Sleep Apnea Syndrome
		Xiong Xin, Yang Xinliang, Luo Jianhua, Yi Sanli, He Jianfeng
		DOI: 10.3969/j.issn.0258-8021.2023.05.006
		Sleep apnea syndrome (SAS) is a common sleep disorder. Traditionally, methods such as time-frequency analysis are used to study the abnormality of EEG signals, while ignoring the spatial location information and difference in characteristics. In this paper, the method of microstate analysis was used to analyze the EEG of five sleep stages (W, N1, N2, N3, REM) of healthy and SAS patients, and to explore the temporal and spatial differences in sleep EEG of SAS patients. The sleep EEG of 66 SAS patients and 10 healthy people was selected and the GFP of W-REM was calculated, and the GFP peak data were used for clustering. As a result, four microstates classes were obtained, the four microstate topographic maps were referred as right fronto-left posterior (A), left fronto-right posterior (B), fronto-occipital midline (C) and frontal midline (D), and the microstate parameters (occurrence frequency, average duration, coverage rate) were calculated. In addition, the static properties global explained variance (GEV), dynamic properties (entropy rate), transition probabilities, and symmetry of transition matrices of the microstate sequence were calculated. Finally, the Hurst index was used to evaluate the long-range correlation of microstate sequences. In the W-REM stage, there were significant differences in the frequency, average duration, coverage rate, GEV, conversion probability, entropy rate and Hurst index between the healthy people and SAS patients (P<0.05). The transfer matrix was symmetric (P>0.01). The Hurst index was greater than 0.5, with remote correlation. In conclusion, compared with the healthy people, SAS patients had altered microstate parameters and sequences in the W-REM stage.
		2023 Vol. 42 (5): 563-571 [Abstract] ( 145 ) HTML (1 KB) PDF (2314 KB) ( 228 )

	572	Research on Shockable Rhythm Detection Algorithm Based on Machine Learning
		Zheng Yue, Hou Xingyu, Wu Xiaomei
		DOI: 10.3969/j.issn.0258-8021.2023.05.007
		Automatic external defibrillator (AED) is an important device in saving patients with cardiac arrest (SCA). Shockable advice algorithm (SAA) is the key technology of AED. In this work, our model was trained with a data set including 2 024 segments of shockable rhythms (SHR) and 7 884 segments of non-shockable rhythms (NSHR). We proposed a SAA based on machine learning. Combining 6 effective features selected from 32 features such as time domain, frequency domain and complexity, support vector machine was employed to classify SHR and NSHR. After 500 experiments, the mean value ±standard deviation of sensitivity of the algorithm was 97.62±0.18%, the specificity was 99.15±0.04%, and the accuracy was 98.79±0.08%. The results showed that the SAA proposed in this paper met the requirements of American Heart Association for SAA performance in AED, and it can be used as an AED algorithm module for automatic discrimination of SHR.
		2023 Vol. 42 (5): 572-582 [Abstract] ( 122 ) HTML (1 KB) PDF (1332 KB) ( 230 )

	583	Predictive Model for Nasopharyngeal Carcinoma Recurrence with MRI and Optimized 3D-ResNet18
		Li Jiongyi, Li Bin, Qiu Qianhui, Liu Yibin, Tian Lianfang
		DOI: 10.3969/j.issn.0258-8021.2023.05.008
		Recurrence of nasopharyngeal carcinoma (NPC) after treatment is an important factor of treatment failure, which is extremely harmful to the quality of life, cure rate and even survival rate of patients with NPC. Therefore, effective prediction of nasopharyngeal carcinoma recurrence plays crucial roles in the prognosis of NPC. Magnetic resonance imaging (MRI) has high resolution of soft tissue, which is a preferred inspection method of NPC. Each imaging of NPC in MRI is quite different, and the gray level of lesion tissue is uneven and the boundaries are blurred, which cause the fact that manual segmentation is difficult, costly, and has limited accuracy, while automatic segmentation of NPC lesions in MRI has low accuracy rate. As a result, low-level image feature extraction and computation based on NPC lesion segmentation has low accuracy rate as well. Performance of NPC recurrence prediction model with radiomics feature engineering and traditional machine learning methods is poor. To solve this problem, a nasopharyngeal carcinoma recurrence prediction model was proposed with MRI and Nesterov accelerating gradient optimized 3D-ResNet18. Through the automatic detection of NPC lesion by distance regularized level-set evolution and histogram equalization in MRI, the enhanced imaging data without redundancy was automatically obtained. The improved 3D-ResNet18 network model optimized by Nesterov accelerated gradient algorithm was used to extract the deep features of NPC and achieve recurrence prediction, providing guidance on the patients’ treatment plans. The research was conducted on MRI images of 140 patients with NPC to complete the model training and cross-validation analysis. Recurrence of NPC was predicted with sensitivity, specificity, accuracy, and AUC of 80.0%, 64.6%, 72.3% and 0.75 respectively. The p values of paired t-test comparing 3D-ResNet10 model and Momentum optimization method under the same conditions were 0.040 and 0.006 respectively. The results showed that the predictive model of nasopharyngeal carcinoma recurrence with improved 3D-ResNet18 can effectively predict the nasopharyngeal carcinoma recurrence.
		2023 Vol. 42 (5): 583-593 [Abstract] ( 101 ) HTML (1 KB) PDF (4862 KB) ( 265 )

	594	Flexible Strain-Sensing Electronic Skin with Anisotropic Conductive Properties
		Lang Bo, Zhang Xuehui, Wu Xiaogang, Wang Yanqin, Chen Weiyi
		DOI: 10.3969/j.issn.0258-8021.2023.05.009
		Electronic skins (e-skins) with intelligent sensing abilities for physiological signals have attracted much attention for their promising applications in smart robots, human-machine interfaces, and wearable medical systems. It is crucial to construct e-skins with anisotropic conductive properties to broaden the application scope of flexible sensing e-skins. In this work, a kind of composite hydrogel named Fe₃O₄@MXene/PVA with anisotropic conductive properties was constructed by using flexible polyvinyl alcohol (PVA) as hydrogel matrix. Magnetic induction technology was used to induce the orientation alignment of Fe₃O₄@MXene in the PVA hydrogel. Experimental results showed that when the cross-linking degree of PVA was 1.5% and the mass fraction of PVA was 8 wt%, the tensile modulus of Fe₃O₄@MXene/PVA reached 19.49 kPa and the fracture strain reached 237%. Mechanical properties of the resulted hydrogel were optimized. When the mass fraction of Fe₃O₄@MXene was 0.064 wt%, the electrical conductivity was optimal, the electrical conductivity in the direction parallel was 0.415 S/m and that in the perpendicular direction was 0.319 S/m. Finally, this work verified the feasibility of the composite hydrogel as an electronic skin for realizing real-time monitoring of the bending angle of human wrist joints. The anisotropic morphology and structure of the electronic are close to that of human tissues, and the composite hydrogel has broad application prospects in biomedical, electronic sensing, and other related fields.
		2023 Vol. 42 (5): 594-602 [Abstract] ( 108 ) HTML (1 KB) PDF (8012 KB) ( 164 )

Reviews

	603	Advances in Electrophysiological Research of Spinal Cord Stimulation on Freezing of Gaitin Parkinson′s Disease
		Li Ziyun, Li Jiping, Wei Jing
		DOI: 10.3969/j.issn.0258-8021.2023.05.010
		Freezing of gait (FoG) is a severe symptom of Parkinson's disease (PD), which has a high incidence rate and seriously affects the quality of life of patients. Currently, there are no ideal methods for the treatment of FoG. Spinal cord stimulation (SCS) is a therapeutic way of electrical stimulation of the spinal cord, which has the advantage of causing less surgical trauma. From the perspective of SCS technology to improve the electrophysiological treatment mechanism of FoG, this paper first introduced the abnormal anatomical mechanism of FoG, followed by reviewing the research progress of abnormal electrophysiology of FoG from the level of cortex and deep brain, and then discussed the electrophysiological mechanism of SCS regulation of FoG from the clinical and animal models. Finally, we summarized the limitations and deficiencies of the current SCS and explored application prospects, possible improvement directions, and future development trends of SCS in the treatment of FoG.
		2023 Vol. 42 (5): 603-609 [Abstract] ( 136 ) HTML (1 KB) PDF (814 KB) ( 249 )

	610	Research Status and Progress of Aquatic Animal Robots
		Peng Yong, Zhang Hui, Zhao Yang, Zhao Zheng, Wen Yudong, Han Lingjun
		DOI: 10.3969/j.issn.0258-8021.2023.05.011
		Biological robot is the organism that is used by human to apply intervention signals to regulate the biological behaviors, which achieve the manipulation by human control technology. Biological robot integrates multi-disciplinary theories and technologies. Due to the unique advantages and outstanding features of aquatic animals, such as mobility, concealment of activities, environmental adaptability, and independent energy supply, aquatic animal robot is of scientific research significance and important practical application value. According to neuroscience theory and internationally commonly used biological control methods, this paper reviewed the control methods and mechanisms of aquatic animal robots from three aspects including stimulus receptors, nerve centers and effectors, and elaborated and analyzed the control methods of these three types of aquatic animal robots. Among them, brain control technology is the most effective and essential control method. In this paper, perspectives of the brain control technology and mechanism of aquatic robot were discussed as well.
		2023 Vol. 42 (5): 610-616 [Abstract] ( 99 ) HTML (1 KB) PDF (3063 KB) ( 159 )

	617	Research Progress of Aerosol Sampling Technology and Samplers for Air Pathogenic Microorganisms
		Wang Xueli, Fu Boqiang, Wang Lei, Liu Kun, Wang Jing, Ma Xu
		DOI: 10.3969/j.issn.0258-8021.2023.05.012
		Pathogenic microbial aerosols in the air are important carrier for the transmission of respiratory pathogens, the research and development of efficient,fast and safe sampling technology and sampler have become hotspots in recent years, which is of great significance to the prevention and control of pathogenic microorganisms and their transmission. This paper makes a detailed analysis and comparison of the current aerosol sampling techniques and samplers which are suitable for pathogenic microorganisms in the air, covering the sampling technology principle, aerosol sampler type, the advantages and disadvantages of the sampler, and the performance evaluation. The latest technical developments were reviewed and the facing questions and challenges were discussed. Besides, future prospects are presented.
		2023 Vol. 42 (5): 617-625 [Abstract] ( 119 ) HTML (1 KB) PDF (1996 KB) ( 209 )

	626	Research Progress of Degradable Behavior of Absorbable Biomaterials in Bone Repair
		Ma Jing, Su Xiuyun, Tang Bin, Wang Lin, Pei Guoxian
		DOI: 10.3969/j.issn.0258-8021.2023.05.013
		Degradable orthopedic implants have attracted intensive attention of clinicians and researchers because the application of these implants can avoid the secondary surgery for implant removal after bone repair and the problem of inflammation of permanent implants. However, the inherent properties of biomaterials of the implants, such as degradation rate, mechanical strength, and osteogenic ability, may hinder their clinical translational applications. To solve the bottleneck, many efforts have been made to the regulation of the degradation behavior and osteogenic capacity of biomaterials via coating bioactive materials, optimizing crystal structure or porous structure and doping functional small molecules. Based on the latest research literatures, this review focused on the degradation mechanism of the degradable biomaterials (including metals, ceramics, natural polymers, and synthetic polymers), the regulation methods of their degradation behavior as well as the effects on their mechanical properties and ability of bone regeneration, aiming to provide a comprehensive reference for developing novel biodegradable orthopedic implants whose degradation behavior depend on the formation of new bone.
		2023 Vol. 42 (5): 626-635 [Abstract] ( 136 ) HTML (1 KB) PDF (5082 KB) ( 246 )

Communications

	636	Single-Lead ECG Chest Band Based on Multi-Layer Screen Printing Flexible Dry Electrode
		Jiang Yuchen, Zhang Yue, Cha Xingzeng, Su Ye, Lai Dakun
		DOI: 10.3969/j.issn.0258-8021.2023.05.014

		2023 Vol. 42 (5): 636-640 [Abstract] ( 129 ) HTML (1 KB) PDF (4866 KB) ( 130 )

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